Remote Imaging Applied to Schistosomiasis Control: The Anning River Project

The use of satellite imaging to remotely detect areas of high risk for transmission of infectious disease is an appealing prospect for large-scale monitoring of these diseases. The detection of large-scale environmental determinants of disease risk, often called landscape epidemiology, has been motivated by several authors (Pavlovsky 1966; Meade et al. 1988). The basic notion is that large-scale factors such as population density, air temperature, hydrological conditions, soil type, and vegetation can determine in a coarse fashion the local conditions contributing to disease vector abundance and human contact with disease agents. These large-scale factors can often be remotely detected by sensors or cameras mounted on satellite or aircraft platforms and can thus be used in a predictive model to mark high risk areas of transmission and to target control or monitoring efforts. A review of satellite technologies for this purpose was recently presented by Washino and Wood (1994) and Hay (1997) and Hay et al. (1997)

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Landslides

Landslides - Investigation and Monitoring offers a comprehensive overview of recent developments in the field of mass movements and landslide hazards. Chapter authors use in situ measurements, modeling, and remotely sensed data and methods to study landslides. This book provides a thorough overview of the latest efforts by international researchers on landslides and opens new possible research directions for further novel developments

Earth resources: A continuing bibliography with indexes

This bibliography lists 579 reports, articles, and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

GEE-Based Ecological Environment Variation Analysis under Human Projects in Typical China Loess Plateau Region

The China Loess Plateau (CLP) is a unique geomorphological unit with abundant coal resources but a fragile ecological environment. Since the implementation of the Western Development plan in 2000, the Grain for Green Project (GGP), coal mining, and urbanization have been extensively promoted by the government in the CLP. However, research on the influence of these human projects on the ecological environment (EE) is still lacking. In this study, we investigated the spatial–temporal variation of EE in a typical CLP region using a Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE). We obtained a long RSEI time series from 2002–2022, and used trend analysis and rescaled range analysis to predict changing trends in EE. Finally, we used Geodetector to verify the influence of three human projects (GGP, coal mining, and urbanization). Our results show that GGP was the major driving factor of ecological changes in the typical CLP region, while coal mining and urbanization had significant local effects on EE. Our research provides valuable support for ecological protection and sustainable social development in the relatively underdeveloped region of northwest China

Landslide mapping from aerial photographs using change detection-based Markov random field

Landslide mapping (LM) is essential for hazard prevention, mitigation, and vulnerability assessment. Despite the great efforts over the past few years, there is room for improvement in its accuracy and efficiency. Existing LM is primarily achieved using field surveys or visual interpretation of remote sensing images. However, such methods are highly labor-intensive and time-consuming, particularly over large areas. Thus, in this paper a change detection-based Markov random field (CDMRF) method is proposed for near-automatic LM from aerial orthophotos. The proposed CDMRF is applied to a landslide-prone site with an area of approximately 40 km2 on Lantau Island, Hong Kong. Compared with the existing region-based level set evolution (RLSE), it has three main advantages: 1) it employs a more robust threshold method to generate the training samples; 2) it can identify landslides more accurately as it takes advantages of both the spectral and spatial contextual information of landslides; and 3) it needs little parameter tuning. Quantitative evaluation shows that it outperforms RLSE in the whole study area by almost 5.5% in Correctness and by 4% in Quality. To our knowledge, it is the first time CDMRF is used to LM from bitemporal aerial photographs. It is highly generic and has great potential for operational LM applications in large areas and also can be adapted for other sources of imagery data

Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

Schistosomiasis control in China : strategy of control and rapid assessment of schistosomiasis risk by remote sensing (RS)and geographic information system (GIS)

Human schistosomiasis remains one of the most prevalent parasitic infections in the tropics and subtropics. The disease currently is endemic in 76 countries and territories and continues to be a major public health concern, especially in the developing world. It is estimated that 650 million people are at risk of infection. Among the 200 million people actually infected, 120 million are symptomatic and 20 million suffer severe disease. Although morbidity control – in line with recommendations put forth by the World Health Organization – has been carried out in China for more than 20 years, it is estimated that 90 million people still live in areas where they are at risk of infection, and 820,000 people are infected with the parasite, i.e. Schistosoma japonicum. The estimated area of intermediate host snail habitats comprise 3,436 km2, concentrated in the 5 lake regions along the Yangtze River that include the provinces of Anhui, Jiangsu, Jiangxi, Hubei and Hunan. The marshlands of the Poyang Lake region represent some of the strongholds for the transmission of S. japonicum. In these settings, for example, the percentages of acute cases and intermediate host snail habitats represent 79.5% and 96.4%, respectively. With the World Bank Loan Project (WBLP) to control schistosomiasis in China, the overall prevalence of S. japonicum was significantly reduced, but in highly endemic areas the re-infection rates are high. In the first part of the present thesis, I summarize the 50-year history of China’s experience and expertise in schistosomiasis control. Particular emphasis is placed on morbidity control and achievements made by the WBLP carried out between 1992 and 2001. Reviewing this body of literature reveals that morbidity control of schistosomiasis in China has been successful, and hence this strategy will continue to form the backbone of protecting people’s health. However, total expenditures have been considerable, and with the termination of the WBLP there is concern that schistosomiasis might re-emerge. In the second part of this thesis, I describe the successful development of a novel compound model to identify the habitats of Oncomelania hupensis, the intermediate host snail of S. japonicum, and hence the identification of high-risk areas of disease transmission. There are three findings that warrant particular notion. First, visual land use classification on multi-temporal Landsat images was performed for preliminary prediction of O. hupensis habitats. Second, extraction of the normalized difference vegetation index and the tasseled cap transformation greenness index were used for improved snail habitat prediction. Third, buffer zones with distances of 600 and 1,200 m were made around the predicted snail habitats to differentiate between high (>15%), moderate (3-15%) and low risk of S. japonicum infection prevalence (< 3%). Preliminary validation of the compound model against ground-based snail surveys in the Poyang Lake region revealed that the model had an excellent predictive ability. The model therefore holds promise for rapid and inexpensive identification of high-risk areas, and can guide subsequent control interventions, such as whether mass or selective chemotherapy should be employed. The model can also be used for diseases surveillance in general and the monitoring of ecological transformations on the transmission dynamics of S. japonicum, for example in the Three Gorges Dam area

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

A characterization of landslide occurrence in the Kigezi Highlands of South Western Uganda

The frequency and magnitude of landslide occurrence in the Kigezi highlands of South Western Uganda has increased, but the key underpinnings of the occurrences are yet to be understood. The overall aim of this study was to characterize the parameters underpinning landslide occurrence in the Kigezi highlands. This information is important for predicting or identifying actual and potential landslide sites. This should inform policy, particularly in terms of developing early warning systems to landslide hazards in these highlands. The present study analysed the area’s topography, soil properties as well as land use and cover changes underpinning the spatialtemporal distribution of landslide occurrence in the region. The present study focussed on selected topographic parameters including slope gradient, profile curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), and Topographic Position Index (TPI). These factors were parameterized in the field and GIS environment using a 10 m Digital Elevation Model. Sixty five landslide features were surveyed and mapped. Soil properties were characterised in relation to slope position. Onsite soil property analysis was conducted within the landslide scars, auger holes and full profile representative sites. Furthermore, soil infiltration and strength tests, as well as clay mineralogy analyses were also conducted. An analysis of the spatial-temporal land use and cover changes was undertaken using satellite imagery spanning the period between 1985 and 2015. Landslides were noted to concentrate along topographic hollows in the landscape. The occurrence is dominant where slope gradient is between 25˚ and 35˚, profile curvature between 0.1 and 5, TWI between 8 and 18, SPI >10 and TPI between -1 and 1. Landslides are less pronounced on slope zones where slope gradient is 45˚, profile curvature 18, SPI 1. Deep soil profiles ranging between 2.5 and 7 meters are a major characteristic of the study area. Soils are characterized by clay pans at a depth ranging between 0.75 and 3 meters within the profiles. The study area is dominated by clay texture, except for the uppermost surface horizons, which are loamy sand. All surface horizons analysed had the percentage of sand, silt and clay ranging from 33 to 55%, 22 to 40% and 10 to 30% respectively. In the deeper horizons, sand was observed to reduce drastically to less than 23%, while clay increased to greater than 50%. The clay content is very high in the deeper horizons exceeding 35%. By implication, such soils with a very high clay content and plasticity index are considered as Vertisols, with a profound influence in the occurrence of landslides. The top soil predominantly contains more quartz, while subsurface horizons have considerable amounts of illite/muscovite as the dominant clay minerals, ranging from 43% to 47 %. The liquid limit, plasticity index, computed weighted plasticity index (PIw), expansiveness (ɛex) and dispersion ranging from 50, 22, 17, 10 and 23 to 66, 44,34,54 and 64, respectively also have strong implications for landslide occurrence. Landslides are not normally experienced during or immediately after extreme rainfall events but occur later in the rainfall season. By implication, this time lag in landslide occurrence and rainfall distribution, is due to the initial infiltration through quartz dominated upper soil layers, before illite/muscovite clays in the lower soil horizons get saturated. Whereas forest cover reduced from 40 % in 1985 to 8% in 2015, cultivated land and settlements increased from 16% and 11% to 52% and 25% respectively during the same period. The distribution of cultivated land decreased in lower slope sections within gradient group < 15˚ by 59%. It however increased in upper sections within gradient cluster 25˚ to 35˚ by over 85% during the study period. There is a shift of cultivated land to the steeper sensitive upper slope elements associated with landslides in the study area. More than 50% of the landslides are occurring on cultivated land, 20% on settlements while less than 15 % and 10% are occurring on grassland and forests with degraded areas respectively. Landslides in Kigezi highlands are triggered by a complex interaction of multiple- factors, including dynamic triggers and ground condition variables. Topographic hollows are convergence zones within the landscape where all the parameters interact to cause landslides. Topographic hollows are therefore potential and actual landslide sites in the study area. Characterized by deep soil horizons with high clay content dominated by illite/muscovite minerals in the sub soils and profile concave forms with moderately steep slopes, topographic hollows are the most vulnerable slope elements to landslide occurrence. The spatial temporal patterns of landslide occurrence in the study area has changed due to increased cultivation of steep middle and upper slopes. Characterized by deep soil horizons with high clay content dominated by illite/muscovite minerals in the sub soils and profile concave forms with moderately steep slopes, topographic hollows are the most vulnerable slope elements to landslide occurrence. The spatial-temporal patterns of landslide occurrence in the study area has changed due to increased cultivation of steep middle and upper slopes. A close spatial and temporal correlation between land use/cover changes and landslide occurrence is discernible. The understanding of these topographical, pedological and land use/cover parameters and their influence on landslide occurrence is important in land management. It is now possible to identify and predict actual and potential landslide zones, and also demarcate safer zones for community activities. The information generated about the area’s topographic, pedological and land cover characteristics should help in vulnerability mitigation and enhance community resilience to landslide hazards in this fragile highland ecosystem. This can be done through designating zones for community activities while avoiding potential landslide zones. It is also recommended that, tree cover restoration be done in the highlands and the farmers encouraged to re-establish terrace farming while avoiding cultivation of sensitive steep middle and upper slope sections